Fahad Saeed

Full Professor of Computing and Lab Director
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Short Bio

Dr. Fahad Saeed is Full Professor and Director of Graduate Studies in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), Miami FL. He received his PhD in the Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC) in 2010. He was trained as a Post-Doctoral Fellow and Research Fellow in the Systems Biology Center at National Institutes of Health (NIH), Bethesda MD from Aug 2010 to January 2014 respectively, under the supervision of Mark Knepper. Prior to joining FIU, Prof. Saeed was a tenure-track Assistant Professor in the Department of Electrical & Computer Engineering and Department of Computer Science at Western Michigan University (WMU), Kalamazoo Michigan since Jan 2014. He was tenured and promoted to the rank of Associate Professor at WMU in August 2018. He has served as a visiting scientist in world-renowned prestigious institutions such as Department of Bio-Systems Science and Engineering (D-BSSE), ETH Zurich, Swiss Institute of Bioinformatics (SIB) and Epithelial Systems Biology Laboratory (ESBL) at National Institutes of Health (NIH) Bethesda, Maryland.

Dr. Saeed’s research interests are at the intersection of machine-learning, high performance computing and real-world applications, especially in computational biology. He is the director of Precision Computational Health and Biomedical Data Science Lab (Saeed Lab) at FIU. His lab develops machine-learning models, combined with high-performance computing, and data science approaches, to study the functional genomics (proteomics), and organization of the human brain and its function in the context of prediction, diagnosis and characterization of biomarkers specific to disorders such as epilepsy, ADHD, Autism, and Alzheimer’s. His research has been funded by NVIDIA, Intel/Altera, Xilinx, National Science Foundation (NSF) and National Institutes of Health (NIH) including the highly prestigious NSF CAREER, and NIH R01 (and R01-equivalent R35 MIRA) grants. More information about his lab research activities can be found at https://pcdslab.github.io/. He also maintains a webpage at https://prof-s.github.io

Complete list of publications is available at: https://scholar.google.com/citations?user=IPXv-GQAAAAJ&hl=en

Honors and Awards

  1. Excellence in Research and Creative Activities Award, Knight Foundation School of Computing and Information Science (KFSCIS), FIU, Dec 2024
  2. FIU Top Scholar, Research and Creative Activities , Florida International University, Sept 2022 CEC News Page
  3. Keynote Speaker at the 14th International Conference on Bioinformatics and Computational Biology (BICOB). More info here: BiCOB-2022 KeyNote Certificate
  4. Excellence in Applied Research Award, School of Computing and Information Science (SCIS), Florida International University (FIU), Dec 2020
  5. Distinguished Research and Creative Scholarship Award, Office of Vice President of Research WMU, Feb 2018
  6. NSF CAREER Award, 2017-2022
  7. ACM Senior Member, May 2017
  8. Outstanding New Researcher Award, College of Engineering and Applied Science (CEAS), Western Michigan University, Jan 2016 (1 faculty member gets the award in a single year for the entire college consisting of 7 academic departments)
  9. IEEE Senior Member, March 2015
  10. NSF CISE Research Initiation Initiative (CRII) Award, Feb 2015 - Feb 2018
  11. Fellows Award for Research Excellence (FARE), National Institutes of Health (NIH), June 2012 (Official award ceremony and US\$1000 travel grant)
  12. Travel award from Swiss Institute of Bioinformatics (SIB), Summers 2009.
  13. Recipient of Think Swiss Scholarship, by the Government of Switzerland for two years (2007 and 2008).
  14. Travel award from D-BSSE ETH Zurich, Summers 2008.

RESEARCH AND EDUCATIONAL INTERESTS

Machine-Learning for health and biomedical data, proteomics, neuroinformatics, computational systems biology, high-performance computing

EDUCATION AND PROFESSIONAL PREPARATION

Research Fellowship, Computational Systems Biology, National Institutes of Health, Bethesda MD. (2011-2014)

Postdoctoral Training, Computational Proteomics, National Institutes of Health, Bethesda MD. (2010-2011)

PhD, Electrical and Computer Engineering, University of Illinois at Chicago, Chicago IL USA. (2006-2010)

BSc Engg, Electrical Engineering, University of Engineering and Technology, Lahore. (2002-2006)

Research

[Method Development] Molecular and Protein Representation Learning

[Method Development] Predicting and Characterizing Alzheimer's Disease & Related Dementias

[analysis] Compressive and reductive analysis of genomic and proteomics data

[method-development] HPC Engine for Mass Spectrometry based Omics Data

[dataset] MLSPred-Bench: Reference EEG Benchmark for Prediction of Epileptic Seizures

[Method Development] Predicting Epileptic Seizures

[Method Development] ML Ecosystem for Mass Spectrometry Data

[Method Development] Characterization and diagnosis of Autism Spectrum

Papers

  1. End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data

  2. A Machine Learning and Benchmarking Approach for Molecular Formula Assignment of Ultra High-Resolution Mass Spectrometry Data from Complex Mixtures

  3. TITAN-BBB: Predicting BBB Permeability using Multi-Modal Deep-Learning Models

  4. MolDeBERTa: Foundational Model for Physicochemical and Structural-Informed Molecular Representation Learning

  5. FiCOPS: Hardware and Software Co-Design of FPGA Computational Framework for Mass Spectrometry-Based Peptide Database Search

  6. Systems and methods for patient-specific epileptic seizure prediction

  7. fairGNN-WOD: fair graph learning without demographics

  8. RAPTOR: Reconfigurable Advanced Platform for Trans- disciplinary Open Research

  9. Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

  10. MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks

  11. Machine-learning models for Alzheimer’s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation

  12. TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

  13. Alzheimer’s disease-associated gene ranking using PhenoGeneRanker

  14. Alzheimer’s disease diagnosis using gray matter of T1-weighted sMRI data and vision transformer

  15. Predicting Individual’s Cognitive Performance Through Multi-Omics Blood Data Using Hierarchical Input Neural Network - HINN

  16. Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels

  17. Lightweight Transformer exhibits comparable performance to LLMs for Seizure Prediction: A case for light-weight models for EEG data

  18. Utilizing Pretrained Vision Transfomers and Large Language Models for Epileptic Seizure Prediction

  19. Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification

  20. PVTAD: Alzheimer’s Disease Diagnosis Using Pyramid Vision Transformer Applied to White Matter of T1-Weighted Structural MRI Data

  21. Making MS Omics Data ML-Ready: SpeCollate Protocols

  22. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study

  23. Communication Evaluation of a Wireless 4-Channel Wearable EEG for Brain-Computer Interface (BCI) and Healthcare Applications

  24. Systems and methods for matching mass spectrometry data with a peptide database

  25. Statistical and Machine Learning Analysis of the Human Brain Functional Network in a Multi-Site Resting-State Functional MRI Database Framework

  26. Q-CASA Invited Speakers Quantum-Centric Supercomputing Strategies for Neuroscience problems: Challenges and Progress

  27. PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

  28. High Performance Computing Algorithms for Accelerating Peptide Identification from Mass-Spectrometry Data Using Heterogeneous Supercomputers

  29. GPU-acceleration of the distributed-memory database peptide search of mass spectrometry data

  30. Energy Efficient AI/ML based Continuous Monitoring at the Edge: ECG and EEG Case Study

  31. Description of Dissolved Organic Matter Transformational Networks at the Molecular Level

  32. Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

  33. ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field

  34. 22nd IEEE International Workshop on High Performance Computational Biology (HiCOMB 2023)

  35. Unsupervised structural classification of dissolved organic matter based on fragmentation pathways

  36. Systems and methods for peptide identification

  37. Systems and methods for measuring similarity between mass spectra and peptides

  38. Systems And Methods For Diagnosing Autism Spectrum Disorder Using fMRI Data

  39. SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning

  40. Re-configurable Hardware for Computational Proteomics

  41. Need for High-Performance Computing for MS-Based Omics Data Analysis

  42. Molecular level characterization of DOM along a freshwater-to-estuarine coastal gradient in the Florida Everglades

  43. Machine-Learning and the Future of HPC for MS-Based Omics

  44. Introduction to Mass Spectrometry Data

  45. High-Performance Computing Strategy Using Distributed-Memory Supercomputers

  46. High-Performance Algorithms for Mass Spectrometry-Based Omics

  47. G-MSR: A GPU-Based Dimensionality Reduction Algorithm

  48. Fast Spectral Pre-processing for Big MS Data

  49. Existing HPC Methods and the Communication Lower Bounds for Distributed-Memory Computations for Mass Spectrometry-Based Omics Data

  50. Computational CPU-GPU Template for Pre-processing of Floating-Point MS Data

  51. Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data

  52. Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks

  53. Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions

  54. A Easy to Use Generalized Template to Support Development of GPU Algorithms

  55. TurboBFS: GPU Based Breadth-First Search (BFS) Algorithms in the Language of Linear Algebra

  56. TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra

  57. SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

  58. Source data: high performance computing framework for tera-scale database search of mass spectrometry data

  59. Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure

  60. Search feasibility in distributed MS-proteomics big data

  61. Real-time peptide identification from high-throughput mass-spectrometry data

  62. Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

  63. Machine Learning methods for diagnosing Autism Spectrum Disorder and Attention-deficit/Hyperactivity Disorder using functional and structural MRI: A Survey

  64. High performance computing framework for tera-scale database search of mass spectrometry data

  65. HiCOPS: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry based Omics Data

  66. Graph Theoretic Approach for the Analysis of Comprehensive Mass-Spectrometry (MS/MS) Data of Dissolved Organic Matter

  67. Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data

  68. DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images

  69. Communication-avoiding micro-architecture to compute Xcorr scores for peptide identification

  70. Benchmarking mass spectrometry based proteomics algorithms using a simulated database

  71. ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data

  72. A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis

  73. ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

  74. NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes’ factors

  75. Methods and systems for compressing data

  76. Federated learning: A survey on enabling technologies, protocols, and applications

  77. Slm-transform: A method for memory-efficient indexing of spectra for database search in lc-ms/ms proteomics

  78. Optimized CNN-based diagnosis system to detect the pneumonia from chest radiographs

  79. NGS‐Integrator: A Tool for Combining Information from Multiple Genome‐Wide NGS Data Tracks Using Minimum Bayes Factors

  80. LBE: A Computational Load Balancing Algorithm for Speeding up Parallel Peptide Search in Mass-Spectrometry based Proteomics

  81. GPU-SFFT: A GPU based parallel algorithm for computing the Sparse Fast Fourier Transform (SFFT) of k-sparse signals

  82. GPU-DFC: A GPU-based parallel algorithm for computing dynamic-functional connectivity of big fMRI data

  83. Efficient shared peak counting in database peptide search using compact data structure for fragment-ion index

  84. Auto-ASD-Network: A technique based on Deep Learning and Support Vector Machines for diagnosing Autism Spectrum Disorder using fMRI data

  85. ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data

  86. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

  87. Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data

  88. Similarity based classification of ADHD using Singular Value Decomposition

  89. Parallel sampling-pipeline for indefinite stream of heterogeneous graphs using OpenCL for FPGAs

  90. MaSS‐Simulator: A Highly Configurable Simulator for Generating MS/MS Datasets for Benchmarking of Proteomics Algorithms

  91. GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data

  92. Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data - An fMRI Study

  93. A Fourier-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

  94. A deep learning-based data minimization algorithm for fast and secure transfer of big genomic datasets

  95. Scalable data structure to compress next-generation sequencing files and its application to compressive genomics

  96. Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs

  97. GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Big fMRI Data

  98. An out-of-core gpu based dimensionality reduction algorithm for big mass spectrometry data and its application in bottom-up proteomics

  99. A new cryptography algorithm to protect cloud-based healthcare services

  100. A Hybrid MPI-OpenMP Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets

  101. Systems-level analysis reveals selective regulation of Aqp2 gene expression by vasopressin

  102. Reductive Analytics on Big MS Data leads to tremendous reduction in time for peptide deduction

  103. MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing

  104. Introduction to the selected papers from the 7th International Conference on Bioinformatics and Computational Biology (BICoB 2015)

  105. GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

  106. Data Aware Communication for Energy Harvesting Sensor Networks

  107. A variable-length network encoding protocol for big genomic data

  108. A Parallel Peptide Indexer and Decoy Generator for Crux Tide using OpenMP

  109. On the sampling of big mass spectrometry data

  110. Design and implementation of network transfer protocol for big genomic data

  111. Big data proteogenomics and high performance computing: Challenges and opportunities

  112. Autophagic degradation of aquaporin-2 is an early event in hypokalemia-induced nephrogenic diabetes insipidus

  113. A parallel algorithm for compression of big next-generation sequencing datasets

  114. Global analysis of the effects of the V2 receptor antagonist satavaptan on protein phosphorylation in collecting duct

  115. Foreword to the special issue on selected papers from the 6th International Conference on Bioinformatics and Computational Biology (BICoB 2014).

  116. Exploiting thread-level and instruction-level parallelism to cluster mass spectrometry data using multicore architectures

  117. Cams-rs: clustering algorithm for large-scale mass spectrometry data using restricted search space and intelligent random sampling

  118. A knowledge base of vasopressin actions in the kidney

  119. 6th International Conference on Bioinformatics and Computational Biology (BICoB 2014)

  120. Quantitative phosphoproteomics implicates clusters of proteins involved in cell‐cell adhesion and transcriptional regulation in the vasopressin signaling network

  121. Proteome-wide measurement of protein half-lives and translation rates in vasopressin-sensitive collecting duct cells

  122. PhosSA: Fast and accurate phosphorylation site assignment algorithm for mass spectrometry data

  123. Foreword to the special issue on selected papers from the 5th International Conference on Bioinformatics and Computational Biology (BICoB 2013)

  124. A high performance algorithm for clustering of large-scale protein mass spectrometry data using multi-core architectures

  125. A Graphical User Interface (GUI) for Phosphorylation Site Assignment of Protein Mass Spectrometry Data

  126. Quantitative phosphoproteomics in nuclei of vasopressin-sensitive renal collecting duct cells

  127. Proteomic and Metabolomic Approaches to Cell Physiology and Pathophysiology: Quantitative phosphoproteomics in nuclei of vasopressin-sensitive renal collecting duct cells

  128. NHLBI-AbDesigner: an online tool for design of peptide-directed antibodies

  129. Identifying protein kinase target preferences using mass spectrometry

  130. High performance phosphorylation site assignment algorithm for mass spectrometry data using multicore systems

  131. Dynamics of the G protein-coupled vasopressin V2 receptor signaling network revealed by quantitative phosphoproteomics

  132. CP hos: a program to calculate and visualize evolutionarily conserved functional phosphorylation sites

  133. An efficient dynamic programming algorithm for phosphorylation site assignment of large-scale mass spectrometry data

  134. An efficient algorithm for clustering of large-scale mass spectrometry data

  135. A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes

  136. Mining temporal patterns from iTRAQ mass spectrometry (LC-MS/MS) data

  137. Mapping‐based temporal pattern mining algorithm (MTPMA) identifies unique clusters of phosphopeptides regulated by vasopressin in collecting duct

  138. Large‐scale iTRAQ‐based quantification of phosphorylation changes during vasopressin signaling

  139. Parallel Algorithm for Center Star Sequence and Alignments with Applications to Short Reads

  140. High performance computational biology algorithms

  141. A graph-theoretic framework for efficient computation of HMM based motif finder

  142. Pyro-align: Sample-align based multiple alignment system for pyrosequencing reads of large number

  143. Multiple sequence alignment system for pyrosequencing reads

  144. An Overview of Multiple Sequence Alignment Systems

  145. A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

  146. Sample-align-d: A high performance multiple sequence alignment system using phylogenetic sampling and domain decomposition

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